Learning from the noise:
semi-mechanistic models for
infectious disease dynamics


Sebastian Funk
20 March, 2017
Imperial College London

Summer 2014

\(y=ax+b\)

\begin{eqnarray} \dot{S}&=&-R_0 \gamma \frac{S}{N}I\\ \dot{I}&=&+R_0 \gamma \frac{S}{N}I - \gamma I\\ \dot{R}&=&+\gamma I \end{eqnarray}

\(y=ax+b\)

A semi-mechanistic model for real-time forecasting

Bayesian state-space models

Bayesian state-space models

Bayesian state-space models

Bayesian state-space models

Bayesian state-space models

Bayesian state-space models

The unknown

  • Community/hospital/funeral transmission
  • Spatial dynamics
  • Changes in behaviour
  • Changes in reporting
  • Interventions
  • Seasonality
  • etc

The known

  • Average incubation period (~9 days)
  • Average infectious period (~11 days)
  • Case-fatality rate (~70%)

WHO Ebola response team (2014)

Transmission intensity as a stochastic process

\(d\log(R_0(t)) = \sigma dW_t\)

Dureau (2013)

Particle MCMC

  • Method for filtering trajectories consistent with data
  • Highly parallelisable

Andrieu (2010), Murray (2013)

Forecasting the Ebola epidemic

Assessing forecasts

Learning from the uncertain

Filtered trajectories tell us something about dynamics

Example: Ebola outbreak in Lofa Country, Liberia

An attempt to tease out factors that controlled Ebola

An attempt to tease out factors that controlled Ebola

Example: age of infection in childhood infections

Outlook

Forecasts are becoming part of outbreak response

Forecasting challenges

Forecasting methodology is underdeveloped

Need methods to select the best model and
combine all available data streams
(individual/spatial/genetic/media)

New tools

New tools

rbi package

Acknowledgements

Anton Camacho, Adam Kucharski, Roz Eggo, John Edmunds (LSHTM)
Bruce Reeder, Etienne Gignoux, Iza Ciglenecki, Amanda Tiffany (MSF)
James Hensman (Lancaster), Lawrence Murray (Uppsala)

Thank you!

http://sbfnk.github.io
@sbfnk